Identification of information security threats using data mining approach in campus network

Abstract

Comprehensive risk assessment implementation in an organization is crucial in order to safeguard valuable organization assets and to minimize information security threats. Thus, inadequate information security risk assessment may result in compromised confidentiality, integrity, and availability of the information system due to unauthorized access particularly in the education domain. Therefore, the objective of this paper is to identify several information security threat risks related to the University Information System. Hence, data from intrusion prevention system (IPS) has been collected from the selected university campus network. Moreover, under Python language, Anaconda is used as a machine learning environment to do the data analysis of the collected data. Basically, the analysis of the university campus network data identified various types of information security threats such as database-related attacks. The contribution of this research is to guide the network administrator to develop an appropriate incident response plan based on the identified threats from the risk assessment activity.